COVID-19 Analysis, Visualization, Comparison and Predictions¶
Introduction to COVID-19¶
Image Credits : Scientific Animations under a CC BY-SA 4.0 license
Coronavirus is a family of viruses that can cause illness, which can vary from common cold and cough to sometimes more severe disease. Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV) were such severe cases with the world already has faced.
SARS-CoV-2 (n-coronavirus) is the new virus of the coronavirus family, which first discovered in 2019, which has not been identified in humans before. It is a contiguous virus which started from Wuhan in December 2019. Which later declared as Pandemic by WHO due to high rate spreads throughout the world. Currently (on the date 15 May 2020), this leads to a total of 300K+ Deaths across the globe, including 159K+ deaths alone in Europe.
Pandemic is spreading all over the world; it becomes more important to understand about this spread. This NoteBook is an effort to analyze the cumulative data of confirmed, deaths, and recovered cases over time. In this notebook, the main focus is to analyze the spread trend of this virus all over the world.
The following two curves shows why we need to flattern the curve and follow the social distancing measures
I will update this notebook continuously with new viz and updated data.¶
NOTE :
- Since Dataset Structure has been changed and recovered dataset is no longer updated by Johns Hopkins University, Few Visualization has been dropped related to recovered cases and also active cases.
- I have also added the Prediction section in this notebook. You can check it out. (Quick Link)
- If you want to see the visualization related to active cases and recovered cases, please see Version 17 of this notebook
- Check out my GitHub repo COVID-19-CaseStudy-and-Predictions
Update log:
- 01 Apr 2020 01:35 AM IST (Version 42) : Indian Testing Data and Comparision with South Korea Added.
- 03 Apr 2020 02:50 AM IST (Version 51) : Calander-Map Added and Visual updates.
- 03 Apr 2020 03:40 PM IST (Version 52) : Dataset Update and Bug fix.
- 05 Apr 2020 01:10 AM IST (Version 54) : 2 New section added COVID-19 Daily Analysis and Testing Data Analysis. Dataset Update and Bug fix.
- 15 Apr 2020 02:50 AM IST (Version 61) : Dataset Update and Prediction model update.
- 02 May 2020 01:50 PM IST (Version 67) : Prediction Model Updated. Dataset Update.
- 03 May 2020 02:05 AM IST (Version 71) : Testing Data Updated.
- 29 May 2020 10:50 PM IST (Version 98) : Dataset Updated.
- 31 May 2020 12:50 AM IST (Version 99) : Dataset Updated.
- 5 June 2020 01:40 AM IST (Version 100) : Dataset Updated.
Please UPVOTE if you LIKE my EFFORT¶
- Check out my GitHub repo COVID-19-CaseStudy-and-Predictions
Follow me:¶
SOURCES:¶
- WHO
- CDC
- Microsoft COVID-19 Tracker
- COVID-19 Tracker by Johns Hopkins University
- COVID-19-India-Tracker
Dataset¶
https://github.com/CSSEGISandData/COVID-19
2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE
This dataset is updated on daily basis by Johns Hopkins CSSE
Please don't PANIC, stay safe, follow your nation and WHO guidelines. We all can defeat this together. Please don't spread rumors.¶
Content :¶
- Intorduction to COVID-19
- Downloding and Installing Prerequisite
- Imports and Datasets
- Preprocessing
- Defining Functions
- General Analysis of Data
- Calander Map New
- Correlation Analysis
- Visualization on Map
- COVID-19 Spread Analysis
- COVID-19 Case (Confirmed, Deaths, Recovered and Active) Percent Split in Different countries (Pie Viz)
- Global Prediction
- COVID-19 Daily Analysis New
- Testing Data Analysis New
- COVID-19 Mortality Rate Variation Over Period of Time
- Comparision of Mortality and Recovery Rate
- Cumulative Confirmed Cases and Cumulative Recovery Vs Cumulative Deaths Analysis
- China Vs Outside China
- COVID-19 : INDIA
- COVID-19 : USA
- Valuable Feedback
If you want to see the visualization related to active cases and recovered cases please see Version 17 of this notebook.
In [1]:
# Installs
!pip install pycountry_convert
!pip install folium
!pip install calmap
# !pip install pandas==0.25.1
!wget https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/models/model_deaths_v4.h5
!wget https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/models/model_confirmed_v4.h5
!wget https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/models/model_usa_c_v2.h5
Imports and Datasets¶
- Pandas - for dataset handeling
- Numpy - Support for Pandas and calculations
- Matplotlib - for visualization (Platting graphas)
- pycountry_convert - Library for getting continent (name) to from their country names
- folium - Library for Map
- keras - Prediction Models
- plotly - for interative plots
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
import pycountry_convert as pc
import folium
import branca
from datetime import datetime, timedelta,date
from scipy.interpolate import make_interp_spline, BSpline
import plotly.express as px
import json, requests
import calmap
from keras.layers import Input, Dense, Activation, LeakyReLU, Dropout
from keras import models
from keras.optimizers import RMSprop, Adam
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE (LINK)¶
Dataset consists of time-series data from 22 JAN 2020 to Till date (Updated on daily Basis).
Three Time-series dataset (Depricated):
- time_series_19-covid-Confirmed.csv (Link Raw File)
- time_series_19-covid-Deaths.csv (Link Raw File)
- time_series_19-covid-Recovered.csv (Link Raw File)
New Time-series dataset:
- time_series_covid19_confirmed_global.csv (Link Raw File)
- time_series_covid19_deaths_global (Link Raw File)
New Dataset (Updated more frequently by web crawler of JHU):
- cases_country.csv (Link Raw File)
In [3]:
# Retriving Dataset
df_confirmed = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
df_deaths = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')
# Depricated
# df_recovered = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv')
df_covid19 = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv")
df_table = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_time.csv",parse_dates=['Last_Update'])
In [4]:
# new dataset
df_covid19 = df_covid19.drop(["People_Tested","People_Hospitalized","UID","ISO3","Mortality_Rate"],axis =1)
df_covid19.head(2)
Out[4]:
In [5]:
df_confirmed.head(2)
Out[5]:
Preprocessing¶
In [6]:
df_confirmed = df_confirmed.rename(columns={"Province/State":"state","Country/Region": "country"})
df_deaths = df_deaths.rename(columns={"Province/State":"state","Country/Region": "country"})
df_covid19 = df_covid19.rename(columns={"Country_Region": "country"})
df_covid19["Active"] = df_covid19["Confirmed"]-df_covid19["Recovered"]-df_covid19["Deaths"]
# df_recovered = df_recovered.rename(columns={"Province/State":"state","Country/Region": "country"})
In [7]:
# Changing the conuntry names as required by pycountry_convert Lib
df_confirmed.loc[df_confirmed['country'] == "US", "country"] = "USA"
df_deaths.loc[df_deaths['country'] == "US", "country"] = "USA"
df_covid19.loc[df_covid19['country'] == "US", "country"] = "USA"
df_table.loc[df_table['Country_Region'] == "US", "Country_Region"] = "USA"
# df_recovered.loc[df_recovered['country'] == "US", "country"] = "USA"
df_confirmed.loc[df_confirmed['country'] == 'Korea, South', "country"] = 'South Korea'
df_deaths.loc[df_deaths['country'] == 'Korea, South', "country"] = 'South Korea'
df_covid19.loc[df_covid19['country'] == "Korea, South", "country"] = "South Korea"
df_table.loc[df_table['Country_Region'] == "Korea, South", "Country_Region"] = "South Korea"
# df_recovered.loc[df_recovered['country'] == 'Korea, South', "country"] = 'South Korea'
df_confirmed.loc[df_confirmed['country'] == 'Taiwan*', "country"] = 'Taiwan'
df_deaths.loc[df_deaths['country'] == 'Taiwan*', "country"] = 'Taiwan'
df_covid19.loc[df_covid19['country'] == "Taiwan*", "country"] = "Taiwan"
df_table.loc[df_table['Country_Region'] == "Taiwan*", "Country_Region"] = "Taiwan"
# df_recovered.loc[df_recovered['country'] == 'Taiwan*', "country"] = 'Taiwan'
df_confirmed.loc[df_confirmed['country'] == 'Congo (Kinshasa)', "country"] = 'Democratic Republic of the Congo'
df_deaths.loc[df_deaths['country'] == 'Congo (Kinshasa)', "country"] = 'Democratic Republic of the Congo'
df_covid19.loc[df_covid19['country'] == "Congo (Kinshasa)", "country"] = "Democratic Republic of the Congo"
df_table.loc[df_table['Country_Region'] == "Congo (Kinshasa)", "Country_Region"] = "Democratic Republic of the Congo"
# df_recovered.loc[df_recovered['country'] == 'Congo (Kinshasa)', "country"] = 'Democratic Republic of the Congo'
df_confirmed.loc[df_confirmed['country'] == "Cote d'Ivoire", "country"] = "Côte d'Ivoire"
df_deaths.loc[df_deaths['country'] == "Cote d'Ivoire", "country"] = "Côte d'Ivoire"
df_covid19.loc[df_covid19['country'] == "Cote d'Ivoire", "country"] = "Côte d'Ivoire"
df_table.loc[df_table['Country_Region'] == "Cote d'Ivoire", "Country_Region"] = "Côte d'Ivoire"
# df_recovered.loc[df_recovered['country'] == "Cote d'Ivoire", "country"] = "Côte d'Ivoire"
df_confirmed.loc[df_confirmed['country'] == "Reunion", "country"] = "Réunion"
df_deaths.loc[df_deaths['country'] == "Reunion", "country"] = "Réunion"
df_covid19.loc[df_covid19['country'] == "Reunion", "country"] = "Réunion"
df_table.loc[df_table['Country_Region'] == "Reunion", "Country_Region"] = "Réunion"
# df_recovered.loc[df_recovered['country'] == "Reunion", "country"] = "Réunion"
df_confirmed.loc[df_confirmed['country'] == 'Congo (Brazzaville)', "country"] = 'Republic of the Congo'
df_deaths.loc[df_deaths['country'] == 'Congo (Brazzaville)', "country"] = 'Republic of the Congo'
df_covid19.loc[df_covid19['country'] == "Congo (Brazzaville)", "country"] = "Republic of the Congo"
df_table.loc[df_table['Country_Region'] == "Congo (Brazzaville)", "Country_Region"] = "Republic of the Congo"
# df_recovered.loc[df_recovered['country'] == 'Congo (Brazzaville)', "country"] = 'Republic of the Congo'
df_confirmed.loc[df_confirmed['country'] == 'Bahamas, The', "country"] = 'Bahamas'
df_deaths.loc[df_deaths['country'] == 'Bahamas, The', "country"] = 'Bahamas'
df_covid19.loc[df_covid19['country'] == "Bahamas, The", "country"] = "Bahamas"
df_table.loc[df_table['Country_Region'] == "Bahamas, The", "Country_Region"] = "Bahamas"
# df_recovered.loc[df_recovered['country'] == 'Bahamas, The', "country"] = 'Bahamas'
df_confirmed.loc[df_confirmed['country'] == 'Gambia, The', "country"] = 'Gambia'
df_deaths.loc[df_deaths['country'] == 'Gambia, The', "country"] = 'Gambia'
df_covid19.loc[df_covid19['country'] == "Gambia, The", "country"] = "Gambia"
df_table.loc[df_table['Country_Region'] == "Gambia", "Country_Region"] = "Gambia"
# df_recovered.loc[df_recovered['country'] == 'Gambia, The', "country"] = 'Gambia'
# getting all countries
countries = np.asarray(df_confirmed["country"])
countries1 = np.asarray(df_covid19["country"])
# Continent_code to Continent_names
continents = {
'NA': 'North America',
'SA': 'South America',
'AS': 'Asia',
'OC': 'Australia',
'AF': 'Africa',
'EU' : 'Europe',
'na' : 'Others'
}
# Defininng Function for getting continent code for country.
def country_to_continent_code(country):
try:
return pc.country_alpha2_to_continent_code(pc.country_name_to_country_alpha2(country))
except :
return 'na'
#Collecting Continent Information
df_confirmed.insert(2,"continent", [continents[country_to_continent_code(country)] for country in countries[:]])
df_deaths.insert(2,"continent", [continents[country_to_continent_code(country)] for country in countries[:]])
df_covid19.insert(1,"continent", [continents[country_to_continent_code(country)] for country in countries1[:]])
df_table.insert(1,"continent", [continents[country_to_continent_code(country)] for country in df_table["Country_Region"].values])
# df_recovered.insert(2,"continent", [continents[country_to_continent_code(country)] for country in countries[:]] )
In [8]:
df_table = df_table[df_table["continent"] != "Others"]
In [9]:
df_deaths[df_deaths["continent" ]== 'Others']
Out[9]:
In [10]:
# df_active = df_confirmed.copy()
# df_active.iloc[:,5:] = df_active.iloc[:,5:] - df_recovered.iloc[:,5:] - df_deaths.iloc[:,5:]
# df_active.head(5)
In [11]:
df_confirmed = df_confirmed.replace(np.nan, '', regex=True)
df_deaths = df_deaths.replace(np.nan, '', regex=True)
# df_recovered = df_recovered.replace(np.nan, '', regex=True)
# df_active = df_active.replace(np.nan, '', regex=True)
In [12]:
def plot_params(ax,axis_label= None, plt_title = None,label_size=15, axis_fsize = 15, title_fsize = 20, scale = 'linear' ):
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=label_size)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.7", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.9", which = 'minor')
# Plot Title
plt.title( plt_title,{'fontsize':title_fsize})
# Yaxis sacle
plt.yscale(scale)
plt.minorticks_on()
# Plot Axes Labels
xl = plt.xlabel(axis_label[0],fontsize = axis_fsize)
yl = plt.ylabel(axis_label[1],fontsize = axis_fsize)
def visualize_covid_cases(confirmed, deaths, continent=None , country = None , state = None, period = None, figure = None, scale = "linear"):
x = 0
if figure == None:
f = plt.figure(figsize=(10,10))
# Sub plot
ax = f.add_subplot(111)
else :
f = figure[0]
# Sub plot
ax = f.add_subplot(figure[1],figure[2],figure[3])
ax.set_axisbelow(True)
plt.tight_layout(pad=10, w_pad=5, h_pad=5)
stats = [confirmed, deaths]
label = ["Confirmed", "Deaths"]
if continent != None:
params = ["continent",continent]
elif country != None:
params = ["country",country]
else:
params = ["All", "All"]
color = ["darkcyan","crimson"]
marker_style = dict(linewidth=3, linestyle='-', marker='o',markersize=4, markerfacecolor='#ffffff')
for i,stat in enumerate(stats):
if params[1] == "All" :
cases = np.sum(np.asarray(stat.iloc[:,5:]),axis = 0)[x:]
else :
cases = np.sum(np.asarray(stat[stat[params[0]] == params[1]].iloc[:,5:]),axis = 0)[x:]
date = np.arange(1,cases.shape[0]+1)[x:]
plt.plot(date,cases,label = label[i]+" (Total : "+str(cases[-1])+")",color=color[i],**marker_style)
plt.fill_between(date,cases,color=color[i],alpha=0.3)
if params[1] == "All" :
Total_confirmed = np.sum(np.asarray(stats[0].iloc[:,5:]),axis = 0)[x:]
Total_deaths = np.sum(np.asarray(stats[1].iloc[:,5:]),axis = 0)[x:]
else :
Total_confirmed = np.sum(np.asarray(stats[0][stat[params[0]] == params[1]].iloc[:,5:]),axis = 0)[x:]
Total_deaths = np.sum(np.asarray(stats[1][stat[params[0]] == params[1]].iloc[:,5:]),axis = 0)[x:]
text = "From "+stats[0].columns[5]+" to "+stats[0].columns[-1]+"\n"
text += "Mortality rate : "+ str(int(Total_deaths[-1]/(Total_confirmed[-1])*10000)/100)+"\n"
text += "Last 5 Days:\n"
text += "Confirmed : " + str(Total_confirmed[-1] - Total_confirmed[-6])+"\n"
text += "Deaths : " + str(Total_deaths[-1] - Total_deaths[-6])+"\n"
text += "Last 24 Hours:\n"
text += "Confirmed : " + str(Total_confirmed[-1] - Total_confirmed[-2])+"\n"
text += "Deaths : " + str(Total_deaths[-1] - Total_deaths[-2])+"\n"
plt.text(0.02, 0.78, text, fontsize=15, horizontalalignment='left', verticalalignment='top', transform=ax.transAxes,bbox=dict(facecolor='white', alpha=0.4))
# Plot Axes Labels
axis_label = ["Days ("+df_confirmed.columns[5]+" - "+df_confirmed.columns[-1]+")","No of Cases"]
# Plot Parameters
plot_params(ax,axis_label,scale = scale)
# Plot Title
if params[1] == "All" :
plt.title("COVID-19 Cases World",{'fontsize':25})
else:
plt.title("COVID-19: "+params[1] ,{'fontsize':25})
# Legend Location
l = plt.legend(loc= "best",fontsize = 15)
if figure == None:
plt.show()
def get_total_cases(cases, country = "All"):
if(country == "All") :
return np.sum(np.asarray(cases.iloc[:,5:]),axis = 0)[-1]
else :
return np.sum(np.asarray(cases[cases["country"] == country].iloc[:,5:]),axis = 0)[-1]
def get_mortality_rate(confirmed,deaths, continent = None, country = None):
if continent != None:
params = ["continent",continent]
elif country != None:
params = ["country",country]
else :
params = ["All", "All"]
if params[1] == "All" :
Total_confirmed = np.sum(np.asarray(confirmed.iloc[:,5:]),axis = 0)
Total_deaths = np.sum(np.asarray(deaths.iloc[:,5:]),axis = 0)
mortality_rate = np.round((Total_deaths/(Total_confirmed+1.01))*100,2)
else :
Total_confirmed = np.sum(np.asarray(confirmed[confirmed[params[0]] == params[1]].iloc[:,5:]),axis = 0)
Total_deaths = np.sum(np.asarray(deaths[deaths[params[0]] == params[1]].iloc[:,5:]),axis = 0)
mortality_rate = np.round((Total_deaths/(Total_confirmed+1.01))*100,2)
return np.nan_to_num(mortality_rate)
def dd(date1,date2):
return (datetime.strptime(date1,'%m/%d/%y') - datetime.strptime(date2,'%m/%d/%y')).days
out = ""#+"output/"
General Analysis of Data¶
Getting country wise and continent wise data.
In [13]:
df_countries_cases = df_covid19.copy().drop(['Lat','Long_','continent','Last_Update'],axis =1)
df_countries_cases.index = df_countries_cases["country"]
df_countries_cases = df_countries_cases.drop(['country'],axis=1)
df_continents_cases = df_covid19.copy().drop(['Lat','Long_','country','Last_Update'],axis =1)
df_continents_cases = df_continents_cases.groupby(["continent"]).sum()
df_countries_cases.fillna(0,inplace=True)
df_continents_cases.fillna(0,inplace=True)
Global Reported Cases till Date¶
Total number of confirmed cases, deaths reported, revoveries and active cases all across the world
In [14]:
df_t = pd.DataFrame(pd.to_numeric(df_countries_cases.sum()),dtype=np.float64).transpose()
df_t["Mortality Rate (per 100)"] = np.round(100*df_t["Deaths"]/df_t["Confirmed"],2)
df_t.style.background_gradient(cmap='Wistia',axis=1).format("{:.2f}").format("{:.0f}",subset=["Confirmed","Deaths","Recovered","Active"])
Out[14]:
Coninent Wise Reported Cases¶
Coninent Wise reported confirmed cases, recovered cases, deaths, active cases
In [15]:
df_continents_cases["Mortality Rate (per 100)"] = np.round(100*df_continents_cases["Deaths"]/df_continents_cases["Confirmed"],2)
df_continents_cases.style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])\
.background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Purples',subset=["Active"])\
.background_gradient(cmap='Pastel1_r',subset=["Incident_Rate"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate (per 100)"])\
.format("{:.2f}")\
.format("{:.0f}",subset=["Confirmed","Deaths","Recovered","Active"])
Out[15]:
Country Wise Reported Cases¶
Country Wise reported confirmed cases, recovered cases, deaths, active cases
In [16]:
# df_countries_cases.sort_values('Confirmed', ascending= False).style.background_gradient(cmap='Wistia')
df_countries_cases["Mortality Rate (per 100)"] = np.round(100*df_countries_cases["Deaths"]/df_countries_cases["Confirmed"],2)
df_countries_cases.sort_values('Confirmed', ascending= False).style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])\
.background_gradient(cmap='Greens',subset=["Recovered"])\
.background_gradient(cmap='Purples',subset=["Active"])\
.background_gradient(cmap='Pastel1_r',subset=["Incident_Rate"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate (per 100)"])\
.format("{:.2f}")\
.format("{:.0f}",subset=["Confirmed","Deaths","Recovered","Active"])
Out[16]:
Top 10 countries (Confirmed Cases and Deaths)¶
In [17]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Confirmed')["Confirmed"].index[-10:],df_countries_cases.sort_values('Confirmed')["Confirmed"].values[-10:],color="darkcyan")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Confirmed Cases",fontsize=18)
plt.title("Top 10 Countries (Confirmed Cases)",fontsize=20)
plt.grid(alpha=0.3)
plt.savefig(out+'Top 10 Countries (Confirmed Cases).png')
In [18]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Deaths')["Deaths"].index[-10:],df_countries_cases.sort_values('Deaths')["Deaths"].values[-10:],color="crimson")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Deaths Cases",fontsize=18)
plt.title("Top 10 Countries (Deaths Cases)",fontsize=20)
plt.grid(alpha=0.3,which='both')
plt.savefig(out+'Top 10 Countries (Deaths Cases).png')
In [19]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Active')["Active"].index[-10:],df_countries_cases.sort_values('Active')["Active"].values[-10:],color="darkorange")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Active Cases",fontsize=18)
plt.title("Top 10 Countries (Active Cases)",fontsize=20)
plt.grid(alpha=0.3,which='both')
plt.savefig(out+'Top 10 Countries (Active Cases).png')
In [20]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Recovered')["Recovered"].index[-10:],df_countries_cases.sort_values('Recovered')["Recovered"].values[-10:],color="limegreen")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Recovered Cases",fontsize=18)
plt.title("Top 10 Countries (Recovered Cases)",fontsize=20)
plt.grid(alpha=0.3,which='both')
plt.savefig(out+'Top 10 Countries (Recovered Cases).png')
Calander Map¶
NOTE: Due to some compatibility issue with pandas this section is no more available. I will soon try to fix the issue.
In [21]:
# f = plt.figure(figsize=(20,10))
# f.add_subplot(2,1,1)
# calmap.yearplot(test['cases'], fillcolor='white', cmap='Blues', linewidth=0.5,linecolor="#fafafa",monthlabels=["J","F","M","A","M","J","J","A","S","O","N","D"])
# # plt.title("Daily Confirmed Cases",fontsize=20)
# # plt.tick_params(labelsize=15)
# f.add_subplot(2,1,2)
# calmap.yearplot(df_table.groupby('Last_Update')['Deaths'].sum().diff(), fillcolor='white', cmap='Reds', linewidth=1,linecolor="#fafafa",year=2020,)
# plt.title("Daily Deaths Cases",fontsize=20)
# plt.tick_params(labelsize=15)
# plt.show()
In [22]:
df_countries_cases.iloc[:,:].corr().style.background_gradient(cmap='Reds').format("{:.3f}")
Out[22]:
Continent Wise Correlation¶
In [23]:
df_continents_cases.iloc[:,:].corr().style.background_gradient(cmap='Reds').format("{:.3f}")
Out[23]:
Visualization on Map¶
In [24]:
world_map = folium.Map(location=[10,0], tiles="cartodbpositron", zoom_start=2,max_zoom=6,min_zoom=2)
for i in range(0,len(df_confirmed)):
folium.Circle(
location=[df_confirmed.iloc[i]['Lat'], df_confirmed.iloc[i]['Long']],
tooltip = "<h5 style='text-align:center;font-weight: bold'>"+df_confirmed.iloc[i]['country']+"</h5>"+
"<div style='text-align:center;'>"+str(np.nan_to_num(df_confirmed.iloc[i]['state']))+"</div>"+
"<hr style='margin:10px;'>"+
"<ul style='color: #444;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
"<li>Confirmed: "+str(df_confirmed.iloc[i,-1])+"</li>"+
"<li>Deaths: "+str(df_deaths.iloc[i,-1])+"</li>"+
"<li>Mortality Rate: "+str(np.round(df_deaths.iloc[i,-1]/(df_confirmed.iloc[i,-1]+1.00001)*100,2))+"</li>"+
"</ul>"
,
radius=(int((np.log(df_confirmed.iloc[i,-1]+1.00001)))+0.2)*50000,
color='#ff6600',
fill_color='#ff8533',
fill=True).add_to(world_map)
world_map
Out[24]:
Global Confirmed Cases Heat Map¶
In [25]:
temp_df = pd.DataFrame(df_countries_cases['Confirmed'])
temp_df = temp_df.reset_index()
fig = px.choropleth(temp_df, locations="country",
color=np.log10(temp_df["Confirmed"]), # lifeExp is a column of gapminder
hover_name="country", # column to add to hover information
hover_data=["Confirmed"],
color_continuous_scale=px.colors.sequential.Plasma,locationmode="country names")
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(title_text="Confirmed Cases Heat Map (Log Scale)")
fig.update_coloraxes(colorbar_title="Confirmed Cases(Log Scale)",colorscale="Blues")
# fig.to_image("Global Heat Map confirmed.png")
fig.show()
Global Deaths Heat Map¶
In [26]:
temp_df = pd.DataFrame(df_countries_cases['Deaths'])
temp_df = temp_df.reset_index()
fig = px.choropleth(temp_df, locations="country",
color=np.log10(temp_df["Deaths"]+1), # lifeExp is a column of gapminder
hover_name="country", # column to add to hover information
hover_data=["Deaths"],
color_continuous_scale=px.colors.sequential.Plasma,locationmode="country names")
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(title_text="Deaths Heat Map (Log Scale)")
fig.update_coloraxes(colorbar_title="Deaths (Log Scale)",colorscale="Reds")
# fig.to_image("Global Heat Map deaths.png")
fig.show()
Global Recovered Heat Map¶
In [27]:
temp_df = pd.DataFrame(df_countries_cases['Recovered'])
temp_df = temp_df.reset_index()
fig = px.choropleth(temp_df, locations="country",
color=np.log10(temp_df.iloc[:,-1]+1), # lifeExp is a column of gapminder
hover_name="country", # column to add to hover information
hover_data=["Recovered"],
color_continuous_scale=px.colors.sequential.Plasma,locationmode="country names")
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(title_text="Recovered Heat Map (Log Scale)")
fig.update_coloraxes(colorbar_title="Recovered",colorscale="Greens")
# fig.to_image("Global Heat Map deaths.png")
fig.show()
COVID-19: Spread Progression¶
In [28]:
df_data = df_table.groupby(['Last_Update', 'Country_Region'])['Confirmed', 'Deaths'].max().reset_index().fillna(0)
df_data["Last_Update"] = pd.to_datetime( df_data["Last_Update"]).dt.strftime('%m/%d/%Y')
fig = px.scatter_geo(df_data, locations="Country_Region", locationmode='country names',
color=np.power(df_data["Confirmed"],0.3)-2 , size= np.power(df_data["Confirmed"]+1,0.3)-1, hover_name="Country_Region",
hover_data=["Confirmed"],
range_color= [0, max(np.power(df_data["Confirmed"],0.3))],
projection="natural earth", animation_frame="Last_Update",
color_continuous_scale=px.colors.sequential.Plasma,
title='COVID-19: Progression of spread'
)
fig.update_coloraxes(colorscale="hot")
fig.update(layout_coloraxis_showscale=False)
fig.show()
COVID-19 Spread Analysis¶
Spread Analysis is in two sections
- Spread Across Globe
- Spread Trends in the World, Continents and few most affected Countries
In [29]:
case_nums_country = df_confirmed.groupby("country").sum().drop(['Lat','Long'],axis =1).apply(lambda x: x[x > 0].count(), axis =0)
d = [datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in case_nums_country.index]
f = plt.figure(figsize=(15,8))
f.add_subplot(111)
marker_style = dict(c="crimson",linewidth=6, linestyle='-', marker='o',markersize=8, markerfacecolor='#ffffff')
plt.plot(d, case_nums_country,**marker_style)
plt.tick_params(labelsize = 14)
plt.xticks(list(np.arange(0,len(d),int(len(d)/5))),d[:-1:int(len(d)/5)]+[d[-1]])
#labels
plt.xlabel("Dates",fontsize=18)
plt.ylabel("Number of Countries/Regions",fontsize=1)
plt.grid(alpha = 0.3)
plt.savefig(out+'spread.png')
plt.show()
plt.close()
2. Spread Trends in the World, Continents and few most affected Countries¶
- COVID-19 Global Spread Trends
- COVID-19 Spread Trends in Different Continents
- COVID-19 Spread Trends in Few Most Affected Countries
- COVID-19 Spread Comparison of few most affected countries
- COVID-19 Spread Comparison of in different continents
1. COVID-19 Global Spread Trends¶
In [30]:
cols = 1
rows = 1
f = plt.figure(figsize=(10,10*rows))
visualize_covid_cases(df_confirmed, df_deaths,continent = "All",figure = [f,rows,cols, 1])
plt.savefig(out+'COIVD-19-World.png')
plt.show()
2 . COVID-19 Spread Trends in Different Continents¶
In [31]:
df_continents= df_confirmed.groupby(["continent"]).sum()
continents = df_continents.sort_values(df_continents.columns[-1],ascending = False).index
cols =2
rows = int(np.ceil(continents.shape[0]/cols))
f = plt.figure(figsize=(20,9*rows))
for i,continent in enumerate(continents):
visualize_covid_cases(df_confirmed, df_deaths, continent = continent,figure = [f,rows,cols, i+1])
plt.show()
3. COVID-19 Spread Trends in Few Most Affected Countries¶
In [32]:
df_countries = df_confirmed.groupby(["country"]).sum()
df_countries = df_countries.sort_values(df_countries.columns[-1],ascending = False)
countries = df_countries[df_countries[df_countries.columns[-1]] >= 4000].index
cols =2
rows = int(np.ceil(countries.shape[0]/cols))
f = plt.figure(figsize=(18,7*rows))
for i,country in enumerate(countries):
visualize_covid_cases(df_confirmed, df_deaths,country = country,figure = [f,rows,cols, i+1])
plt.show()
4. COVID-19 Spread Comparison of few most affected countries and INDIA¶
In [33]:
temp = df_confirmed.groupby('country').sum().drop(["Lat","Long"],axis =1).sort_values(df_confirmed.columns[-1], ascending= False)
threshold = 50
f = plt.figure(figsize=(15,12))
ax = f.add_subplot(111)
for i,country in enumerate(temp.index):
if i >= 9:
if country != "India" and country != "Japan" :
continue
days = 60
t = temp.loc[temp.index== country].values[0]
t = t[t>threshold][:days]
date = np.arange(0,len(t[:days]))
xnew = np.linspace(date.min(), date.max(), 30)
spl = make_interp_spline(date, t, k=1) # type: BSpline
power_smooth = spl(xnew)
if country != "India":
plt.plot(xnew,power_smooth,'-o',label = country,linewidth =3, markevery=[-1])
else:
marker_style = dict(linewidth=4, linestyle='-', marker='o',markersize=10, markerfacecolor='#ffffff')
plt.plot(date,t,"-.",label = country,**marker_style)
plt.tick_params(labelsize = 14)
plt.xticks(np.arange(0,days,7),[ "Day "+str(i) for i in range(days)][::7])
# Reference lines
x = np.arange(0,18)
y = 2**(x+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("No. of cases doubles every day",(x[-2],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,int(days-22))
y = 2**(x/2+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every second day",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,int(days-4))
y = 2**(x/7+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every week",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,int(days-4))
y = 2**(x/30+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every month",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
# India is following trend similar to doulbe the cases in 4 days but it may increase the rate
x = np.arange(0,int(days-5))
y = 2**(x/4+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "Red")
plt.annotate(".. every 4 days",(x[-3],y[-1]),color="Red",xycoords="data",fontsize=14,alpha = 0.8)
# plot Params
plt.xlabel("Days",fontsize=17)
plt.ylabel("Number of Confirmed Cases",fontsize=17)
plt.title("Trend Comparison of Different Countries\n and India (confirmed) ",fontsize=22)
plt.legend(loc = "upper left")
plt.yscale("log")
plt.grid(which="both")
plt.savefig(out+'Trend Comparison with India (confirmed).png')
plt.show()
In [34]:
temp = df_deaths.groupby('country').sum().drop(["Lat","Long"],axis =1).sort_values(df_deaths.columns[-1], ascending= False)
threshold = 10
f = plt.figure(figsize=(15,12))
ax = f.add_subplot(111)
for i,country in enumerate(temp.index):
if i >= 10:
break
days = 60
t = temp.loc[temp.index== country].values[0]
t = t[t>threshold][:days]
date = np.arange(0,len(t[:days]))
xnew = np.linspace(date.min(), date.max(), 30)
spl = make_interp_spline(date, t, k=1) # type: BSpline
power_smooth = spl(xnew)
plt.plot(xnew,power_smooth,'-o',label = country,linewidth =3, markevery=[-1])
plt.tick_params(labelsize = 14)
plt.xticks(np.arange(0,days,7),[ "Day "+str(i) for i in range(days)][::7])
# Reference lines
x = np.arange(0,18)
y = 2**(x+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("No. of cases doubles every day",(x[-2],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-22)
y = 2**(x/2+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every second day",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-3)
y = 2**(x/7+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every week",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-3)
y = 2**(x/30+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every month",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
# plot Params
plt.xlabel("Days",fontsize=17)
plt.ylabel("Number of Deaths",fontsize=17)
plt.title("Trend Comparison of Different Countries \n(Deaths)",fontsize=22)
plt.legend(loc = "upper left")
plt.yscale("log")
plt.grid(which="both")
plt.savefig(out+'Trend Comparison countries deaths.png')
plt.show()
5. COVID-19 Spread Comparison of in different continents¶
In [35]:
temp = df_confirmed.groupby('continent').sum().drop(["Lat","Long"],axis =1).sort_values(df_confirmed.columns[-1], ascending= False)
threshold = 50
f = plt.figure(figsize=(15,12))
ax = f.add_subplot(111)
for i,country in enumerate(temp.index):
if i >= 10:
break
days = 60
t = temp.loc[temp.index== country].values[0]
t = t[t>threshold][:days]
date = np.arange(0,len(t[:days]))
xnew = np.linspace(date.min(), date.max(), 30)
spl = make_interp_spline(date, t, k=1) # type: BSpline
power_smooth = spl(xnew)
plt.plot(xnew,power_smooth,'-o',label = country,linewidth =3, markevery=[-1])
plt.tick_params(labelsize = 14)
plt.xticks(np.arange(0,days,7),[ "Day "+str(i) for i in range(days)][::7])
# Reference lines
x = np.arange(0,18)
y = 2**(x+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("No. of cases doubles every day",(x[-2],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-22)
y = 2**(x/2+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every second day",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-2)
y = 2**(x/7+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every week",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-3)
y = 2**(x/30+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every month",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
# plot Params
plt.xlabel("Days",fontsize=17)
plt.ylabel("Number of Confirmed Cases",fontsize=17)
plt.title("Trend Comparison of Different Continents \n(confirmed) ",fontsize=22)
plt.legend(loc = "upper left")
plt.yscale("log")
plt.grid(which="both")
plt.savefig(out+'Trend Comparison of continents (Confirmed).png')
plt.show()
In [36]:
temp = df_deaths.groupby('continent').sum().drop(["Lat","Long"],axis =1).sort_values(df_deaths.columns[-1], ascending= False)
threshold = 10
f = plt.figure(figsize=(15,12))
ax = f.add_subplot(111)
for i,country in enumerate(temp.index):
if i >= 10:
break
days = 60
t = temp.loc[temp.index== country].values[0]
t = t[t>threshold][:days]
date = np.arange(0,len(t[:days]))
xnew = np.linspace(date.min(), date.max(), 10)
spl = make_interp_spline(date, t, k=1) # type: BSpline
power_smooth = spl(xnew)
plt.plot(xnew,power_smooth,'-o',label = country,linewidth =3, markevery=[-1])
plt.tick_params(labelsize = 14)
plt.xticks(np.arange(0,days,7),[ "Day "+str(i) for i in range(days)][::7])
# Reference lines
x = np.arange(0,18)
y = 2**(x+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("No. of cases doubles every day",(x[-2],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-22)
y = 2**(x/2+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every second day",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-3)
y = 2**(x/7+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every week",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days-3)
y = 2**(x/30+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. every month",(x[-3],y[-1]),xycoords="data",fontsize=14,alpha = 0.5)
# plot Params
plt.xlabel("Days",fontsize=17)
plt.ylabel("Number of Deaths",fontsize=17)
plt.title("Trend Comparison of Different Continents \n(Deaths)",fontsize=22)
plt.legend(loc = "upper left")
plt.yscale("log")
plt.grid(which="both")
plt.savefig(out+'Trend Comparison continents (deaths).png')
plt.show()
Global Prediction¶
Global Trend:¶
It is useful to understand the global trend of an increase in the number of cases over time. There is always a pattern in any data, but the concern is how strongly data follows a pattern. COVID-19 spreads exponentially, positive cases of COVID-19 takes 67 days to reach 1 Lakhs while it takes only 11 days to reach 2 Lakhs, 4 days to reach 3 Lakhs, and just 2 days to reach 5 Lakhs. This trend shows how fast it spreads.
Note: This prediction is only based on the current spread trend, and no other factor is included, so this trend may vary a lot. Also, at some point in time, the testing capacity will become the limiting factor, so it may affect the actual figure a lot. This is to estimate the possible values that could reach with the consideration of any measures taken by different countries to control the spread.
In [37]:
temp_data = df_confirmed.iloc[:,5:].sum(axis =0)
f = plt.figure(figsize=(20,12))
f.add_subplot(111)
threshold = 100000
t = temp_data.values
t = t[t >threshold]
date = np.arange(0,len(t[:]))
xnew = np.linspace(date.min(), date.max(), 10)
spl = make_interp_spline(date, t, k=1) # type: BSpline
power_smooth = spl(xnew)
marker_style = dict(linewidth=4, linestyle='-', marker='o',markersize=10, markerfacecolor='#ffffff')
plt.plot(date,t,"-.",label="Confirmed Cases",**marker_style)
days = 80
# Reference lines
x = np.arange(0,days)
y = 2**(x+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("No. of Cases Doubles Every Day",(np.log2((t.max()-threshold)/threshold),t.max()-threshold/2),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days)
y = 2**(x/3+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("...Every Third Day",(np.log2((t.max()-threshold)/threshold)*3,t.max()-threshold),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days)
y = 2**(x/7+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate("... Every Week",(np.log2((t.max()-threshold)/threshold)*7,t.max()-threshold),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days)
y = 2**(x/30+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "gray")
plt.annotate(".. Every Month",(18,2**(17/30+np.log2(threshold))),xycoords="data",fontsize=14,alpha = 0.5)
x = np.arange(0,days)
y = 2**(x/11+np.log2(threshold))
plt.plot(x,y,"--",linewidth =2,color = "Red")
plt.annotate(".. Every 11 Days",(np.log2((t.max()-threshold)/threshold)*9,t.max()-threshold),color="Red",xycoords="data",fontsize=14,alpha = 0.8)
plt.xlim(date[0],date[-1])
plt.ylim(threshold - threshold/10,2*t.max())
# plot Params
# plt.tight_layout()
plt.tick_params(labelsize = 16)
plt.xticks(np.arange(0,len(t[:])+7,7),[ "Day "+str(i) for i in range(len(t[:])+7)][::7])
plt.xlabel("Days",fontsize=19)
plt.ylabel("Number of Confirmed Cases",fontsize=19)
plt.title("World Trend",fontsize=24)
plt.legend(loc = "upper left")
plt.yscale("log")
plt.grid(which="both")
plt.savefig(out+"World Trend Confirmed cases.png")
plt.show()
Prediction Curve for Global Confirmed Cases¶
Buliding Model
In [38]:
# Visible = Input(shape=(1,))
# Dense_l1 = Dense(80,name="Dense_l1")(Visible)
# LRelu_l1 = LeakyReLU(name = "LRelu_l1")(Dense_l1)
# Dense_l2 = Dense(80,name = "Dense_l2")(LRelu_l1)
# LRelu_l2 = LeakyReLU(name = "LRelu_l2")(Dense_l2)
# Dense_l3 = Dense(80,name = "Dense_l3")(LRelu_l2)
# LRelu_l3 = LeakyReLU(name = "LRelu_l3")(Dense_l3)
# Dense_l4 = Dense(1,name="Dense_l4")(LRelu_l3)
# LRelu_l4 = LeakyReLU(name = "Output")(Dense_l4
# )
# model = models.Model(inputs=Visible, outputs=LRelu_l4)
# model.compile(optimizer=Adam(lr=0.001),
# loss='mean_squared_error',
# metrics=['accuracy'])
# model.summary()
In [39]:
data_y = np.log10(np.asarray(df_confirmed.iloc[:,5:].sum(axis =0)).astype("float32"))
data_x = np.arange(1,len(data_y)+1)
In [40]:
# epochs = 1000
# model.fit(data_x.reshape([data_y.shape[0],1]),data_y.reshape([data_y.shape[0],1]),epochs=epochs)
In [41]:
# model.save('model_confirmed_v4.h5')
Loading Model¶
In [42]:
model = models.load_model("model_confirmed_v4.h5")
model.summary()
In [43]:
lakh = 100000
prediction_days = 10
temp_data = df_confirmed.iloc[:,5:].sum(axis =0)
data = np.power(10,model.predict(np.arange(1,len(temp_data)+prediction_days+1)))
f = plt.figure(figsize=(15,10))
ax = f.add_subplot(111)
date = np.arange(0,len(temp_data))
marker_style = dict(linewidth=3, linestyle='-', marker='o',markersize=7, markerfacecolor='#ffffff')
plt.plot(date,temp_data/lakh,"-.",color="darkcyan",**marker_style, label="Actual Curve")
date = np.arange(0,len(data))
plt.plot(date,data/lakh,"-.",color="orangered",label="Predicted Curve")
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total = d + nextdays
text = "Prediction for next "+str(prediction_days) +" days:\n"
for i in range(prediction_days):
text += nextdays[i]+" : "+str(np.round(data[-1*(prediction_days-i)],-3)[0]/lakh)+" L\n"
plt.text(0.02, 0.78, text, fontsize=17, horizontalalignment='left', verticalalignment='top', transform=ax.transAxes,bbox=dict(facecolor='white', alpha=0.4))
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=14)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.7", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.9", which = 'minor')
# Plot Title
plt.title("COVID-19 Next 10 day Prediction Curve-Global Confirmed Cases",{'fontsize':22})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Confirmed Cases (Lakh)",fontsize =18)
# plt.yscale("log")
plt.legend(fontsize =18)
plt.tick_params(labelsize = 13)
plt.savefig(out+"Prediction Curve-Confirmed.png")
plt.show()
Prediction Curve for Global Death Cases¶
Buliding Model
In [44]:
# Visible = Input(shape=(1,))
# Dense_l1 = Dense(80,name="Dense_l1")(Visible)
# LRelu_l1 = LeakyReLU(name = "LRelu_l1")(Dense_l1)
# Drop1 = Dropout(0.2)(LRelu_l1)
# Dense_l2 = Dense(80,name = "Dense_l2")(Drop1)
# LRelu_l2 = LeakyReLU(name = "LRelu_l2")(Dense_l2)
# Drop2 = Dropout(0.2)(LRelu_l2)
# Dense_l3 = Dense(80,name = "Dense_l3")(Drop2)
# LRelu_l3 = LeakyReLU(name = "LRelu_l3")(Dense_l3)
# Drop3 = Dropout(0.2)(LRelu_l3)
# Dense_l4 = Dense(1,name="Dense_l4")(Drop3)
# LRelu_l4 = LeakyReLU(name = "Output")(Dense_l4)
# model2 = models.Model(inputs=Visible, outputs=LRelu_l4)
# model2.compile(optimizer=Adam(lr=0.001),
# loss='mean_squared_error',
# metrics=['accuracy'])
# model2.summary()
In [45]:
data1_y = np.log10(np.asarray(df_deaths.iloc[:,5:].sum(axis =0)).astype("float32"))
data1_x = np.arange(1,len(data1_y)+1)
In [46]:
# epochs=1000
# model2.fit(data1_x.reshape([data1_y.shape[0],1]),data1_y.reshape([data1_y.shape[0],1]),epochs=epochs)
In [47]:
# model2.save('model_deaths_v4.h5')
Loading Model¶
In [48]:
model2 = models.load_model("model_deaths_v4.h5")
model2.summary()
In [49]:
thousand = 1000
prediction_days = 10
temp_data = df_deaths.iloc[:,5:].sum(axis =0)
data = np.power(10,model2.predict(np.arange(1,len(temp_data)+prediction_days+1)))
f = plt.figure(figsize=(15,10))
ax = f.add_subplot(111)
date = np.arange(0,len(temp_data))
# xnew = np.linspace(date.min(), date.max(), 10)
# spl = make_interp_spline(date, t, k=1) # type: BSpline
# power_smooth = spl(xnew)
marker_style = dict(linewidth=3, linestyle='-', marker='o',markersize=7, markerfacecolor='#ffffff')
plt.plot(date,temp_data/thousand,"-.",color="maroon",**marker_style, label="Actual Curve")
date = np.arange(0,len(data))
plt.plot(date,data/thousand,"-.",color="orangered",label="Predicted Curve")
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
text = "Prediction for next "+str(prediction_days) +" days:\n"
for i in range(prediction_days):
text += nextdays[i]+" : "+str(np.round(data[-1*(prediction_days-i)],-1)[0]/thousand)+"K\n"
plt.text(0.02, 0.78, text, fontsize=17, horizontalalignment='left', verticalalignment='top', transform=ax.transAxes,bbox=dict(facecolor='white', alpha=0.4))
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=12)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.7", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.9", which = 'minor')
# Plot Title
plt.title("COVID-19 Next 10 day Prediction Curve-Global Deaths Cases",{'fontsize':22})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Deaths Cases (Thousand)",fontsize =18)
# plt.yscale("log")
plt.legend(fontsize =18)
plt.tick_params(labelsize = 13)
plt.savefig(out+"Prediction Curve2.png")
plt.show()
Tabulation of prediction and actual figure after 20th May-Global¶
In [50]:
start_date = "5/20/20"
sd= dd(start_date,"1/22/20")
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
data_confirmed = np.power(10,model.predict(np.arange(1,len(data_y)+prediction_days+1)))
data_deaths = np.power(10,model2.predict(np.arange(1,len(data_y)+prediction_days+1)))
pd.DataFrame([total[sd:],
list(np.int64(np.round(data_confirmed[sd:].reshape(-1)/lakh,2)*lakh)),
list(df_confirmed.iloc[:,5:].sum(axis =0)[sd:]),
list(np.int64(np.round(data_deaths[sd:].reshape(-1)/thousand,2)*thousand)),
list(df_deaths.iloc[:,5:].sum(axis =0)[sd:])],
["Date","Confirmed(Predicted)","Confirmed(Actual)","Deaths(Predicted)","Deaths(Actual)"]
).transpose().set_index("Date")
Out[50]:
In [51]:
# Data
temp_data = np.nan_to_num(df_confirmed.sum()[5:].diff())
# Plot
f = plt.figure(figsize=(15,10))
ax = f.add_subplot(111)
date = np.arange(0,len(temp_data))
marker_style = dict(linewidth=2, linestyle='-', marker='o',markersize=5)
plt.plot(date,temp_data/thousand,"-.",color="red",**marker_style)
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=12)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.85", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.95", which = 'minor')
# Plot Title
plt.title("COVID-19 Global Daily New Confirmed Cases",{'fontsize':22})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Daily Confirmed Cases (Thousand)",fontsize =18)
# plt.yscale("log")
plt.tick_params(labelsize = 13)
plt.savefig(out+"daily confirmed cases global.png")
plt.show()
In [52]:
# Data
temp_data = np.nan_to_num(df_deaths.sum()[5:].diff())
# Plot
f = plt.figure(figsize=(15,10))
ax = f.add_subplot(111)
date = np.arange(0,len(temp_data))
marker_style = dict(linewidth=2, linestyle='-', marker='o',markersize=5)
plt.plot(date,temp_data/thousand,"-.",color="red",**marker_style)
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=14)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.85", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.95", which = 'minor')
# Plot Title
plt.title("COVID-19 Global Daily Deaths Reported",{'fontsize':22})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Daily Deaths Reported (Thousand)",fontsize =18)
# plt.yscale("log")
plt.savefig(out+"daily deaths cases Global.png")
plt.show()
2.Daily New Cases in Different Countries¶
In [53]:
thoudand = 1000
temp = df_confirmed.groupby('country').sum().diff(axis=1).sort_values(df_deaths.columns[-1],ascending =False).head(10).replace(np.nan,0)
threshold = 0
f = plt.figure(figsize=(20,12))
ax = f.add_subplot(111)
for i,country in enumerate(temp.index):
t = temp.loc[temp.index== country].values[0]
t = t[t>=threshold]
date = np.arange(0,len(t[:]))
plt.plot(date,t/thoudand,'-o',label = country,linewidth =2, markevery=[-1])
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=14)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.85", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.95", which = 'minor')
# Plot Title
plt.title("COVID-19 Daily Confirmed Cases in Different Countries",{'fontsize':24})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Daily Confirmed Cases (Thousand)",fontsize =18)
# plt.yscale("log")
plt.legend(fontsize=18)
plt.savefig(out+"daily confirmed cases countrywise.png")
plt.show()
In [54]:
thoudand = 1000
temp = df_deaths.groupby('country').sum().diff(axis=1).sort_values(df_deaths.columns[-1],ascending =False).head(10).replace(np.nan,0)
threshold = 0
f = plt.figure(figsize=(20,12))
ax = f.add_subplot(111)
for i,country in enumerate(temp.index):
t = temp.loc[temp.index== country].values[0]
t = t[t>=threshold]
date = np.arange(0,len(t[:]))
plt.plot(date,t/thoudand,'-o',label = country,linewidth =2, markevery=[-1])
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=15)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.85", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.95", which = 'minor')
# Plot Title
plt.title("COVID-19 Daily Deaths Reported in Different Countries",{'fontsize':24})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Daily Deaths Reported (Thousand)",fontsize =18)
# plt.yscale("log")
plt.legend(fontsize=18)
plt.savefig(out+"daily deaths reported countrywise.png")
plt.show()
Testing Analysis¶
Dataset : Link
Testing is important in controling the spread. As it helps controls further spread of disease by any individual once the suspect found positive, by isolation or quarantine. This section, provides insights based on the data provided by https://ourworldindata.org. https://github.com/owid/covid-19-data
In [55]:
df_test = pd.read_csv("https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/test_data_v70/test_data.csv")[:-1]
# df_test
In [56]:
df_test.drop(df_test[df_test["Entity"]=="India - people tested"].index[0],inplace=True)
df_test["country"] = [ c.split(" ")[:-3] for c in df_test["Entity"].values]
df_test = df_test.loc[:,["country","Cumulative total","Cumulative total per thousand"]]
In [57]:
df_test = df_test.sort_values("Cumulative total",ascending=False)[:23].reset_index().drop(['index'],axis=1)
df_test["country"] = [ c[0] for c in df_test["country"].values]
df_test.loc[0,"country"] ="USA"
df_test.loc[7,"country"] = "United Kingdom"
df_test.loc[12,"country"] = "South Korea"
df_test = df_test.drop([4,10,14])
df_test = df_test.reset_index().drop(['index'],axis=1)
df_test["Cumulative total per million"] = df_test["Cumulative total per thousand"]*1000
df_test = df_test.drop(["Cumulative total per thousand"],axis=1)
In [58]:
df_test["confirmed"] = [ df_countries_cases.loc[c,'Confirmed'] for c in df_test["country"].values]
df_test["deaths"] = [ df_countries_cases.loc[c,'Deaths'] for c in df_test["country"].values]
df_test["MR"] = np.round(100*df_test["deaths"]/df_test["confirmed"],2)
df_test["Positive"] = np.round(100*df_test["confirmed"]/df_test["Cumulative total"],2)
df_test.style.background_gradient(cmap='Blues',subset=["Cumulative total"])\
.background_gradient(cmap='Reds',subset=["Cumulative total per million"])\
.background_gradient(cmap='Greens',subset=["confirmed"])\
.background_gradient(cmap='Purples',subset=["deaths"])\
.background_gradient(cmap='YlOrBr',subset=["MR"])\
.background_gradient(cmap='bone_r',subset=["Positive"])\
.format("{:.0f}",subset=["confirmed","deaths","Cumulative total","Cumulative total per million"])\
.format("{:.2f}",subset=["MR","Positive"])
Out[58]:
Correlation¶
In [59]:
df_test.corr().style.background_gradient(cmap='Blues').format("{:.2f}")
Out[59]:
In [60]:
fig = px.bar(df_test.sort_values("Cumulative total"),
x='country', y="Cumulative total",
text = "MR",
hover_name="country",
hover_data=["confirmed","deaths","Cumulative total","Positive"],
title='COVID-19: Tests Over Countries',
)
fig.update_xaxes(title_text="Country")
fig.update_yaxes(title_text="Number of Tests (Text on bars is MR %)")
fig.show()
In [61]:
fig = px.bar(df_test.sort_values("Positive"),
x='country', y="Positive",
text = "MR",
hover_name="country",
hover_data=["confirmed","deaths","Cumulative total","Positive"],
title='COVID-19: Test(Positive) Over Countries',
)
fig.update_xaxes(title_text="Country")
fig.update_yaxes(title_text="Positive Tests(%)- (Text on bars is MR %)")
fig.show()
In [62]:
fig = px.bar(df_test.sort_values("MR"),
x='country', y="MR",
text = "Positive",
hover_name="country",
hover_data=["confirmed","deaths","Cumulative total","Positive"],
title='COVID-19: MR Over Countries',
)
fig.update_xaxes(title_text="Country")
fig.update_yaxes(title_text="MR(%)- (Text on bars is Positive %)")
fig.show()
In [63]:
fig = px.bar(df_test.sort_values("Cumulative total per million"),
x='country', y="Cumulative total per million",
text = "MR",
hover_name="country",
hover_data=["confirmed","deaths","Cumulative total","Positive"],
title='COVID-19: Tests per million Over Countries',
)
fig.update_xaxes(title_text="Country")
fig.update_yaxes(title_text="Tests per million(%)- (Text on bars is MR %)")
fig.show()
Total Test vs Positive (per 100 Test)¶
In [64]:
fig = px.scatter(df_test, y=df_test.loc[:,"MR"],
x= df_test.loc[:,"Positive"],
color= "country", hover_name="country",
hover_data=["confirmed","deaths","Cumulative total"],
color_continuous_scale=px.colors.sequential.Plasma,
title='COVID-19: Test(Positive) vs Mortality rate',
size = np.power(df_test["confirmed"]+1,0.3)-0.5,
size_max = 30,
height =600,
)
fig.update_coloraxes(colorscale="hot")
fig.update(layout_coloraxis_showscale=False)
fig.update_yaxes(title_text="Mortality Rate (%)")
fig.update_xaxes(title_text="Tests Positive (%)")
fig.show()
COVID-19 Case (Confirmed and Deaths) Percent Split in Different countries (Pie Viz)¶
These pie plots are showing the proportion of the spread of this Pandemic across the world. Each pie is showing a few major contributor countries, and rest countries are shown as "Others". Criteria :
- Confirmed Cases: Any countries reported more than 20000 confirmed cases are shown the rest of the countries are under "others" Category.
- Deaths: Any Countries reported more than 1000 deaths are shown rest of the countries are under "others" Category.
- Active Cases: Any Countries reported more than 15000 active cases are shown the rest of the countries are under "others" Category.
- Recovered Cases: Any Countries reported more than 2000 recoveries are shown rest of the countries are under "others" Category.
In [65]:
rows = 4
f = plt.figure(figsize=(15,10*rows))
stats = [df_covid19.loc[:,['country','Confirmed']],df_covid19.loc[:,['country','Deaths']],df_covid19.loc[:,['country','Active']],df_covid19.loc[:,['country','Recovered']]]
label = ["Confirmed","Deaths","Active","Recovered"]
threshold = [25000,1000,15000,5000]
for i, stat in enumerate(stats):
plt.tight_layout()
df_countries = stat.groupby(["country"]).sum()
df_countries = df_countries.sort_values(df_countries.columns[-1],ascending= False)
others = df_countries[df_countries[df_countries.columns[-1]] < threshold[i] ].sum()[-1]
df_countries = df_countries[df_countries[df_countries.columns[-1]] > threshold[i]]
df_countries = df_countries[df_countries.columns[-1]]
df_countries["others"] = others
labels = [df_countries.index[i] +" (" + str(int(df_countries[i])) +") "for i in range(df_countries.shape[0])]
ax = f.add_subplot(rows,1,i+1)
plt.pie(df_countries, labels=labels,autopct='%1.1f%%',pctdistance=0.85, labeldistance=1.1,textprops = {'fontsize':10.5})
my_circle=plt.Circle( (0,0), 0.7, color='white')
p=plt.gcf()
p.gca().add_artist(my_circle)
plt.text(0.5,0.5,"World Total "+label[i]+ " COVID-19 Cases\n"+str(stat.sum().values[1]), horizontalalignment='center',verticalalignment='center',transform=ax.transAxes, size=18, alpha = 0.6)
plt.show()
COVID-19 Mortality Rate Variation Over Period of Time¶
For any epidemic the one of the most important evaluation is Mortality Rate. It is the measure of number of deaths in a particular population during a specific interval.
- 1st curve shows how the mortality rate varies from 22 JAN 2020 to till date all over the world.
- 2nd Curve shows the variation of mortality rate in different continents over time.
In [66]:
df_continents= df_confirmed.groupby(["continent"]).sum()
continents = df_continents.sort_values(df_continents.columns[-1],ascending = False).index
continents = ["All"]+list(continents)
cols =1
rows = 2
axis_label = ["Days ("+df_confirmed.columns[5]+" - "+df_confirmed.columns[-1]+")","Mortality Rate (of 100)"]
f = plt.figure(figsize=(15,10*rows))
#SubPlot 1
ax = f.add_subplot(211)
mortality_rate = get_mortality_rate(df_confirmed,df_deaths,continent=continents[0])
plt.plot(np.arange(1,mortality_rate.shape[0]+1),mortality_rate,label = "World : Current Mortality Rate "+str(mortality_rate[-1]),linewidth=3)
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
plt_title = "COVID-19: World Mortality Rate Curve"
plot_params(ax,axis_label,plt_title)
# Legend Location
l = plt.legend(loc= "best")
#SubPlot 2
ax = f.add_subplot(212)
for i, continent in enumerate(continents[1:]):
mortality_rate = get_mortality_rate(df_confirmed,df_deaths,continent=continent)
# mortality_rate = mortality_rate.replace(np.nan, 0, regex=True)
plt.plot(np.arange(1+mortality_rate[mortality_rate == 0].shape[0],mortality_rate[mortality_rate == 0].shape[0]+mortality_rate[mortality_rate > 0].shape[0]+1),mortality_rate[mortality_rate > 0],label = continents[i+1]+" "+str(mortality_rate[-1]),linewidth=3)
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
plt_title = "COVID-19: Mortality Rate Curve for all Continents"
plot_params(ax,axis_label,plt_title)
# Legend Location
l = plt.legend(loc= "best")
plt.minorticks_on()
plt.savefig(out+'Mortality rate.png')
plt.show()
COVID-19: Change in Mortality Rate of Each Countries Over Time¶
In [67]:
df_data = df_table.groupby(['Last_Update', 'Country_Region'])['Confirmed', 'Deaths','continent'].max().reset_index().fillna(0)
df_data["Last_Update"] = pd.to_datetime( df_data["Last_Update"]).dt.strftime('%m/%d/%Y')
fig = px.scatter(df_data, y=100*df_data["Deaths"]/(df_data["Confirmed"]+1),
x= df_data["Confirmed"]+1,
range_y = [-1,18],
range_x = [1,df_data["Confirmed"].max()+10000],
color= "continent", hover_name="Country_Region",
hover_data=["Confirmed","Deaths"],
range_color= [0, max(np.power(df_data["Confirmed"],0.3))],
animation_frame="Last_Update",
animation_group="Country_Region",
color_continuous_scale=px.colors.sequential.Plasma,
title='COVID-19: Change in Mortality Rate of Each Countries Over Time',
size = np.power(df_data["Confirmed"]+1,0.3)-0.5,
size_max = 30,
log_x=True,
height =700,
)
fig.update_coloraxes(colorscale="hot")
fig.update(layout_coloraxis_showscale=False)
fig.update_xaxes(title_text="Confirmed Cases (Log Scale)")
fig.update_yaxes(title_text="Mortality Rate (%)")
fig.show()
Comparision of Mortality and Recovery Rate¶
Note :
- Depricated: Check out version 17
In any Epidemic or Pandemic, the study of Mortality Rate and Recovery Rate has an important role. This plot compares the variation of MR and RR over time through the World.
Scale : LOG
In [68]:
# cols =1
# rows = 2
# axis_label = ["Days ("+df_confirmed.columns[5]+" - "+df_confirmed.columns[-1]+")","Mortality Rate/ Recovery Rate (of 100)"]
# f = plt.figure(figsize=(15,10*rows))
# #SubPlot 1
# ax = f.add_subplot(211)
# mortality_rate = get_mortality_rate(df_confirmed,df_deaths,continent=continents[0])
# recovered_rate = get_mortality_rate(df_confirmed,df_recovered,continent=continents[0])
# plt.plot(np.arange(1,mortality_rate.shape[0]+1),mortality_rate,label = "World : Current Mortality Rate "+str(mortality_rate[-1]))
# plt.plot(np.arange(1,recovered_rate.shape[0]+1),recovered_rate,label = "World : Current Recovery Rate "+str(recovered_rate[-1]))
# plt_title = "COVID-19: World Mortality and Recovery Rate Comparision"
# plot_params(ax,axis_label,plt_title,scale = "log")
# # Legend Location
# l = plt.legend(loc= "best")
Cumulative Confirmed Cases and Cumulative Recovery Vs Cumulative Deaths Analysis¶
The variation of Cumulative Confirmed Cases and Cumulative Recovery with Cumulative Deaths can show a trend. These 2 curves depict the same. Also, these curves should be a straight line as shown in the 1st curve, but the 2nd curve is not showing that trend, and as the number of recovered cases is increasing, death is increasing at a faster rate.
- 1st curve: Cumulative Confirmed Cases VS Cumulative Deaths
- 2nd curve: Cululative Recovery VS Cumulative Deaths
NOTE :
- Cumulative Recovery Vs Cumulative Deaths has been depricated. Check out version 17
In [69]:
cols =1
rows = 2
f = plt.figure(figsize=(15,10*rows))
# SubPlot 1
ax = f.add_subplot(211)
plt.plot(np.sum(np.asarray(df_confirmed.iloc[:,5:]),axis = 0),np.sum(np.asarray(df_deaths.iloc[:,5:]),axis = 0))
axis_label = ["Cumulative Confirmed Cases","Cumulative Deaths"]
plt_title = "COVID-19: World - \nCumulative Confirmed Cases Vs Cumulative Deaths Curve"
plot_params(ax,axis_label,plt_title)
# # SubPlot 2
# ax = f.add_subplot(212)
# mortality_rate = get_mortality_rate(df_confirmed,df_deaths,continent=continents[0])
# plt.plot(np.sum(np.asarray(df_recovered.iloc[:,5:]),axis = 0),np.sum(np.asarray(df_deaths.iloc[:,5:]),axis = 0))
# axis_label = ["Cumulative Recoveries","Cumulative Deaths"]
# plt_title = "COVID-19: World - Cumulative Recovery Vs Cumulative Deaths Curve"
# plot_params(ax,axis_label,plt_title)
plt.minorticks_on()
plt.savefig(out+'Cumulative Confirmed Cases Vs Cumulative Deaths Curve.png')
plt.show()
Variation of Deaths vs Confirmed cases of different cuntries over time¶
In [70]:
df_data = df_table.groupby(['Last_Update', 'Country_Region'])['Confirmed', 'Deaths','continent'].max().reset_index().fillna(0)
df_data["Last_Update"] = pd.to_datetime( df_data["Last_Update"]).dt.strftime('%m/%d/%Y')
fig = px.scatter(df_data, y=df_data["Deaths"],
x= df_data["Confirmed"]+1,
range_y = [1,df_data["Deaths"].max()+1000],
range_x = [1,df_data["Confirmed"].max()+10000],
color= "continent", hover_name="Country_Region",
hover_data=["Confirmed","Deaths"],
range_color= [0, max(np.power(df_data["Confirmed"],0.3))],
animation_frame="Last_Update",
animation_group="Country_Region",
color_continuous_scale=px.colors.sequential.Plasma,
title='COVID-19: Change Deaths vs Cofirmed of Each Countries Over Time',
size = np.power(df_data["Confirmed"]+1,0.3)-0.5,
size_max = 30,
log_x=True,
log_y=True,
height =700,
)
fig.update_coloraxes(colorscale="hot")
fig.update(layout_coloraxis_showscale=False)
fig.update_xaxes(title_text="Confirmed Cases (Log Scale)")
fig.update_yaxes(title_text="Deaths Rate (Log Scale)")
fig.show()
China Vs Outside China¶
In [71]:
# Getting data for China and Oouside China
# df - Cumulative data for china, outside china and world total (confirmed cases)
# df_newcases- new cases reported per day (confirmed cases)
df_confirmed_report = df_confirmed.copy()
df_confirmed_report.loc[df_confirmed_report['country'] != "China","country"] = "Outside China"
df_confirmed_report = df_confirmed_report.groupby("country").sum().drop(["Lat","Long"],axis =1)
df_confirmed_report.loc["Total"] = df_confirmed_report.sum()
df_confirmed_newcases = df_confirmed_report.groupby(level =0).diff(axis =1)
df_confirmed_newcases = df_confirmed_newcases.replace(np.nan, 0, regex=True)
f = plt.figure(figsize=(15,8))
ax1 = f.add_subplot(111)
ax1.bar(df_confirmed_report[df_confirmed_report.index == "China"].columns,df_confirmed_newcases[df_confirmed_newcases.index == "China"].values[0], label = "China (New)",color='dodgerblue')
ax1.bar(df_confirmed_report[df_confirmed_report.index == "Outside China"].columns,df_confirmed_newcases[df_confirmed_newcases.index == "Outside China"].values[0],bottom=df_confirmed_newcases[df_confirmed_newcases.index == "China"].values[0],label = "Outside China (New)",color='orangered')
# Labels
ax1.set_xlabel("Dates",fontsize=17)
ax1.set_ylabel("New Cases Reported",fontsize =17)
ax1.tick_params(size=10,labelsize=15)
ax1.set_xticks(np.arange(0.5, len(df_confirmed_report.columns), 6))
ax1.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_confirmed_report.columns][::6],fontsize=15)
l = np.arange(0, df_confirmed_report.max(axis = 1)[2]/10+10000, 5000)
ax1.set_yticks(l[::int(len(l)/5)])
# ax1.spines['bottom'].set_position('zero')
ax2 = ax1.twinx()
marker_style = dict(linewidth=6, linestyle='-', marker='o',markersize=10, markerfacecolor='#ffffff')
ax2.plot(df_confirmed_report[df_confirmed_report.index == "Total"].columns ,df_confirmed_report[df_confirmed_report.index == "Total"].values[0],**marker_style,label = "World Total (Cumulative)",color="darkorange",clip_on=False)
ax2.plot(df_confirmed_report[df_confirmed_report.index == "China"].columns ,df_confirmed_report[df_confirmed_report.index == "China"].values[0],**marker_style,label = "China (Cumulative)",color="limegreen",clip_on=False)
ax2.plot(df_confirmed_report[df_confirmed_report.index == "Outside China"].columns ,df_confirmed_report[df_confirmed_report.index == "Outside China"].values[0],**marker_style,label ="Outside China (Cumulative)",color="darkviolet",clip_on=False)
ax2.bar([0],[0])
# Label
ax2.tick_params(labelsize=15)
ax2.set_ylabel("Cumulative",fontsize =17)
ax2.set_xticks(np.arange(0.5, len(df_confirmed_report.columns), 6))
ax2.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_confirmed_report.columns][::6])
l = np.arange(0, df_confirmed_report.max(axis = 1)[2]+100000, 100000)
ax2.set_yticks(l[::int(len(l)/5)])
f.tight_layout()
f.legend(loc = "upper left", bbox_to_anchor=(0.1,0.95))
plt.title("COVID-19 Confirmed Cases: China Vs Rest of the World",fontsize = 22)
plt.savefig(out+'China vs Rest of the world.png')
plt.show()
In [72]:
# Getting data for China and Oouside China
# df - Cumulative data for china, outside china and world total (deaths)
# df_newcases- new cases reported per day (deaths)
df_deaths_report = df_deaths.copy()
df_deaths_report.loc[df_deaths_report['country'] != "China","country"] = "Outside China"
df_deaths_report = df_deaths_report.groupby("country").sum().drop(["Lat","Long"],axis =1)
df_deaths_report.loc["Total"] = df_deaths_report.sum()
df_deaths_newcases = df_deaths_report.groupby(level =0).diff(axis =1)
df_deaths_newcases = df_deaths_newcases.replace(np.nan, 0, regex=True)
f = plt.figure(figsize=(15,8))
ax1 = f.add_subplot(111)
ax1.bar(df_deaths_report[df_deaths_report.index == "China"].columns,df_deaths_newcases[df_deaths_newcases.index == "China"].values[0], label = "China (New)",color='dodgerblue')
ax1.bar(df_deaths_report[df_deaths_report.index == "Outside China"].columns,df_deaths_newcases[df_deaths_newcases.index == "Outside China"].values[0],bottom=df_deaths_newcases[df_deaths_newcases.index == "China"].values[0],label = "Outside China (New)",color='orangered')
# Labels
ax1.set_xlabel("Dates",fontsize=17)
ax1.set_ylabel("New Cases Reported",fontsize =17)
ax1.tick_params(size=10,labelsize=15)
ax1.set_xticks(np.arange(0.5, len(df_deaths_report.columns), 6))
ax1.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_deaths_report.columns][::6],fontsize=15)
l = np.arange(0, df_deaths_report.max(axis = 1)[2]/10+500, 500)
ax1.set_yticks(l[::int(len(l)/5)])
# ax1.spines['bottom'].set_position('zero')
ax2 = ax1.twinx()
marker_style = dict(linewidth=6, linestyle='-', marker='o',markersize=10, markerfacecolor='#ffffff')
ax2.plot(df_deaths_report[df_deaths_report.index == "Total"].columns ,df_deaths_report[df_deaths_report.index == "Total"].values[0],**marker_style,label = "World Total (Cumulative)",color="darkorange",clip_on=False)
ax2.plot(df_deaths_report[df_deaths_report.index == "China"].columns ,df_deaths_report[df_deaths_report.index == "China"].values[0],**marker_style,label = "China (Cumulative)",color="limegreen",clip_on=False)
ax2.plot(df_deaths_report[df_deaths_report.index == "Outside China"].columns ,df_deaths_report[df_deaths_report.index == "Outside China"].values[0],**marker_style,label ="Outsdte China (Cumulative)",color="darkviolet",clip_on=False)
ax2.bar([0],[0])
# Label
ax2.tick_params(labelsize=15)
ax2.set_ylabel("Cumulative",fontsize =17)
ax2.set_xticks(np.arange(0.5, len(df_deaths_report.columns), 6))
ax2.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_deaths_report.columns][::6])
l = np.arange(0, df_deaths_report.max(axis = 1)[2]+4000, 2000)
ax2.set_yticks(l[::int(len(l)/5)])
f.tight_layout()
f.legend(loc = "upper left", bbox_to_anchor=(0.1,0.95))
plt.title("COVID-19 Deaths Reported: China Vs Rest of the World",fontsize = 22)
plt.savefig(out+'China vs Rest of the world1.png')
plt.show()
NOTE :
- This section Depricated. Check out version 17
In [73]:
# # Getting data for China and Oouside China
# # df - Cumulative data for china, outside china and world total (deaths)
# # df_newcases- new cases reported per day (deaths)
# df_recovered_report = df_recovered.copy()
# df_recovered_report.loc[df_recovered_report['country'] != "China","country"] = "Outside China"
# df_recovered_report = df_recovered_report.groupby("country").sum().drop(["Lat","Long"],axis =1)
# df_recovered_report.loc["Total"] = df_recovered_report.sum()
# df_recovered_newcases = df_recovered_report.groupby(level =0).diff(axis =1)
# f = plt.figure(figsize=(15,8))
# ax1 = f.add_subplot(111)
# ax1.bar(df_recovered_report[df_recovered_report.index == "China"].columns,df_recovered_newcases[df_recovered_newcases.index == "China"].values[0], label = "China (New)",color='dodgerblue')
# ax1.bar(df_recovered_report[df_recovered_report.index == "Outside China"].columns,df_recovered_newcases[df_recovered_newcases.index == "Outside China"].values[0],bottom=df_recovered_newcases[df_recovered_newcases.index == "China"].values[0],label = "Outside China (New)",color='orangered')
# # Labels
# ax1.set_xlabel("Dates",fontsize=17)
# ax1.set_ylabel("New Cases Reported",fontsize =17)
# ax1.tick_params(size=10,labelsize=15)
# ax1.set_xticks(np.arange(0.5, len(df_recovered_report.columns), 6))
# ax1.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_recovered_report.columns][::6],fontsize=15)
# ax1.set_yticks(np.arange(0, df_recovered_newcases.max(axis = 1)[2]+1000, 1000))
# # ax1.spines['bottom'].set_position('zero')
# ax2 = ax1.twinx()
# marker_style = dict(linewidth=6, linestyle='-', marker='o',markersize=10, markerfacecolor='#ffffff')
# ax2.plot(df_recovered_report[df_recovered_report.index == "Total"].columns ,df_recovered_report[df_recovered_report.index == "Total"].values[0],**marker_style,label = "World Total (Cumulative)",color="darkorange",clip_on=False)
# ax2.plot(df_recovered_report[df_recovered_report.index == "China"].columns ,df_recovered_report[df_recovered_report.index == "China"].values[0],**marker_style,label = "China (Cumulative)",color="limegreen",clip_on=False)
# ax2.plot(df_recovered_report[df_recovered_report.index == "Outside China"].columns ,df_recovered_report[df_recovered_report.index == "Outside China"].values[0],**marker_style,label ="Outside China (Cumulative)",color="darkviolet",clip_on=False)
# ax2.bar([0],[0])
# # Label
# ax2.tick_params(labelsize=15)
# ax2.set_ylabel("Cumulative",fontsize =17)
# ax2.set_xticks(np.arange(0.5, len(df_recovered_report.columns), 6))
# ax2.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_recovered_report.columns][::6])
# ax2.set_yticks(np.arange(0, df_recovered_report.max(axis = 1)[2]+10000, 10000))
# f.tight_layout()
# f.legend(loc = "upper left", bbox_to_anchor=(0.1,0.95))
# plt.title("COVID-19 Recovered Cases: China Vs Rest of the World",fontsize = 22)
# plt.savefig('China vs Rest of the world2.png')
# plt.show()
In [74]:
# pd.DataFrame([df_confirmed_report.iloc[:,-1],df_deaths_report.iloc[:,-1],df_recovered_report.iloc[:,-1]],["Confirmed","Deaths","Recovered"]).transpose()
In [75]:
f = plt.figure(figsize=(15,8))
ax = f.add_subplot(111)
ax.bar(0.05+np.arange(len(df_confirmed_newcases[df_confirmed_newcases.index == "China"].columns)),df_confirmed_newcases[df_confirmed_newcases.index == "China"].values[0], label = "Confirmed Cases",color='dodgerblue',width = 0.45)
ax.bar(0.5+np.arange(len(df_deaths_newcases[df_deaths_newcases.index == "China"].columns)),df_deaths_newcases[df_deaths_newcases.index == "China"].values[0], label = "Deaths",color='red',width = 0.45)
# ax.bar(0.67+np.arange(len(df_recovered_newcases[df_recovered_newcases.index == "China"].columns)),df_recovered_newcases[df_recovered_newcases.index == "China"].values[0], label = "Recoveries",color='orange',width = 0.33)
# ax.bar(df_recovered_report[df_recovered_report.index == "Outside China"].columns,df_newcases[df_newcases.index == "Outside China"].values[0],bottom=df_newcases[df_newcases.index == "China"].values[0],label = "Outside China (New)",color='orangered')
# Labels
ax.set_xlabel("Dates",fontsize=17)
ax.set_ylabel("Cases Reported",fontsize =17)
ax.tick_params(labelsize=15)
ax.set_xticks(np.arange(0.5, len(df_confirmed_newcases.columns), 6))
ax.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_confirmed_newcases.columns][::6],fontsize=15)
ax.set_yticks(np.arange(0, df_confirmed_newcases.loc["China"].max()+4000, 4000))
ax.legend(loc = "upper left", bbox_to_anchor=(0.1,0.95))
plt.title("COVID-19 Cases: China",fontsize = 22)
plt.grid(alpha = 0.3)
plt.savefig(out+'China vs Rest of the world3.png')
plt.show()
In [76]:
f = plt.figure(figsize=(15,8))
ax = f.add_subplot(111)
ax.bar(0.05+np.arange(len(df_confirmed_newcases[df_confirmed_newcases.index == "Outside China"].columns)),df_confirmed_newcases[df_confirmed_newcases.index == "Outside China"].values[0], label = "Confirmed Cases",color='dodgerblue',width = 0.45)
ax.bar(0.5+np.arange(len(df_deaths_newcases[df_deaths_newcases.index == "Outside China"].columns)),df_deaths_newcases[df_deaths_newcases.index == "Outside China"].values[0], label = "Deaths",color='red',width = 0.45)
# ax.bar(0.67+np.arange(len(df_recovered_newcases[df_recovered_newcases.index == "Outside China"].columns)),df_recovered_newcases[df_recovered_newcases.index == "Outside China"].values[0], label = "Recoveries",color='orange',width = 0.33)
# ax.bar(df_recovered_report[df_recovered_report.index == "Outside China"].columns,df_newcases[df_newcases.index == "Outside China"].values[0],bottom=df_newcases[df_newcases.index == "China"].values[0],label = "Outside China (New)",color='orangered')
# Labels
ax.set_xlabel("Dates",fontsize=17)
ax.set_ylabel("Cases Reported",fontsize =17)
ax.tick_params(labelsize=15)
ax.set_xticks(np.arange(0, len(df_confirmed_newcases.columns), 6))
ax.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_confirmed_newcases.columns][::6],fontsize=15)
ax.set_yticks(np.arange(0, df_confirmed_newcases.loc["Outside China"].max()+10000, 20000))
ax.legend(loc = "upper left", bbox_to_anchor=(0.1,0.95))
plt.title("COVID-19 Cases: Rest of the World",fontsize = 22)
plt.grid(alpha = 0.3)
plt.savefig(out+'China vs Rest of the world4.png')
plt.show()
In [77]:
f = plt.figure(figsize=(15,8))
ax = f.add_subplot(111)
ax.bar(0.05+np.arange(len(df_confirmed_newcases[df_confirmed_newcases.index == "Total"].columns)),df_confirmed_newcases[df_confirmed_newcases.index == "Total"].values[0], label = "Confirmed Cases",color='dodgerblue',width = 0.45)
ax.bar(0.5+np.arange(len(df_deaths_newcases[df_deaths_newcases.index == "Total"].columns)),df_deaths_newcases[df_deaths_newcases.index == "Total"].values[0], label = "Deaths",color='red',width = 0.45)
# ax.bar(0.67+np.arange(len(df_recovered_newcases[df_recovered_newcases.index == "Total"].columns)),df_recovered_newcases[df_recovered_newcases.index == "Total"].values[0], label = "Recoveries",color='orange',width = 0.33)
# ax.bar(df_recovered_report[df_recovered_report.index == "Outside China"].columns,df_newcases[df_newcases.index == "Outside China"].values[0],bottom=df_newcases[df_newcases.index == "China"].values[0],label = "Outside China (New)",color='orangered')
# Labels
ax.set_xlabel("Dates",fontsize=17)
ax.set_ylabel("Cases Reported",fontsize =17)
ax.tick_params(labelsize=15)
ax.set_xticks(np.arange(0, len(df_confirmed_newcases.columns), 6))
ax.set_xticklabels([datetime.strptime(date,'%m/%d/%y').strftime("%d %b") for date in df_confirmed_newcases.columns][::6],fontsize=15)
ax.set_yticks(np.arange(0, df_confirmed_newcases.loc["Total"].max()+10000, 20000))
ax.legend(loc = "upper left", bbox_to_anchor=(0.1,0.95))
plt.title("COVID-19 Cases: All over the Globe",fontsize = 22)
plt.grid(alpha = 0.3)
plt.savefig(out+'China vs Rest of the world5.png')
plt.show()
COVID-19 : INDIA¶
Few basic visualization related to India. I will be updating with more visualization in further commits. i am also working on Paitent Data Insights. Adding Soon
Dataset:¶
This dataset is provided by https://api.rootnet.in/
Analysis of Tests done in India and Comparission with South Korea¶
Note: I am working on more on testing data and comparisions insights and will adding soon.
In [78]:
df_india_test = pd.io.json.json_normalize(requests.get('https://api.rootnet.in/covid19-in/stats/testing/history').json()['data']).rename(columns = {"totalIndividualsTested":"c_individualtest","totalPositiveCases":"c_positive","totalSamplesTested":"c_tests"})
In [79]:
df_india_test["p2t_ratio"]= np.round(100*df_india_test["c_positive"]/df_india_test["c_tests"],2)
df_india_test["positive"] = df_india_test["c_positive"].diff()
df_india_test["tests"] = df_india_test["c_tests"].diff()
df_india_test["p2t_ratio"]= np.round(100*df_india_test["positive"]/df_india_test["tests"],2)
df_india_test = df_india_test[1:]
In [80]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.bar(df_india_test["day"],df_india_test["tests"].values[:],color="darkcyan",label="Test (Negetive)"+str())
plt.bar(df_india_test["day"],df_india_test["positive"].values[:],bottom=df_india_test["tests"].values[:]-df_india_test["positive"].values[:],color="Red",label="Test (Positive)")
plt.tick_params(size=5,labelsize = 13)
plt.tick_params(axis="x",size=5,labelsize = 13,labelrotation=60 )
plt.xlabel("Date",fontsize=18)
plt.ylabel("Test Results",fontsize=18)
plt.title("Tests in India",fontsize=20)
plt.grid(alpha=0.3)
plt.legend()
plt.savefig(out+'Test_India.png')
Total tests done till date (1 April 2020) in India¶
In [81]:
df_india_test["c_tests"][-1:].values[0]
Out[81]:
Test Conducted per Million People¶
In [82]:
np.round(1000000*df_india_test["c_tests"][-1:].values[0]/1300000000,2)
Out[82]:
This is far less than the tests happening in South Korea, Singapore and Other countries. Here is a comparison table for India and South Korea
(Last Update: 31 Mar 2020)
| Parameters | India | South Korea |
|---|---|---|
| Population | 130 Cr | 5.15 Cr |
| First Case Reported | 28 Jan 2020 | 23 Jan 2020 |
| Total Tests | 37000 | 395194 |
| Tests Per Million Peoples | 28.46 | 7674 |
| Positives | 1250 | 9661 |
| No. Positives in 100 Tests | 3.38 | 2.44 |
| Current Mortality Rate | 3.76 | 1.63 |
From this table, this can be concluded that India is far behind in terms of testing, and testing is very important to keep track of COVID19 spread and also to quarantine those people or isolate them to control the spread of COVID19 as done by South Korea.
COVID19 Cases in India¶
In [83]:
india_data_json = requests.get('https://api.rootnet.in/covid19-in/unofficial/covid19india.org/statewise').json()
df_india = pd.io.json.json_normalize(india_data_json['data']['statewise'])
df_india = df_india.set_index("state")
In [84]:
total = df_india.sum()
total.name = "Total"
df_t = pd.DataFrame(total,dtype=float).transpose()
df_t["Mortality Rate (per 100)"] = np.round(100*df_t["deaths"]/df_t["confirmed"],2)
df_t.style.background_gradient(cmap='Purples',axis=1)
Out[84]:
In [85]:
df_india["Mortality Rate (per 100)"]= np.round(np.nan_to_num(100*df_india["deaths"]/df_india["confirmed"]),2)
df_india.style.background_gradient(cmap='Blues',subset=["confirmed"])\
.background_gradient(cmap='Reds',subset=["deaths"])\
.background_gradient(cmap='Greens',subset=["recovered"])\
.background_gradient(cmap='Purples',subset=["active"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate (per 100)"])\
.format("{:.2f}",subset=["Mortality Rate (per 100)"])
Out[85]:
States with Reported Deaths¶
In [86]:
df_india[df_india['deaths'] > 0].style.background_gradient(cmap='Blues',subset=["confirmed"])\
.background_gradient(cmap='Reds',subset=["deaths"])\
.background_gradient(cmap='Greens',subset=["recovered"])\
.background_gradient(cmap='Purples',subset=["active"])\
.background_gradient(cmap='YlOrBr',subset=["Mortality Rate (per 100)"])\
.format("{:.2f}",subset=["Mortality Rate (per 100)"])
Out[86]:
10 Most effected States¶
In [87]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_india.sort_values('confirmed')["confirmed"].index[-10:],df_india.sort_values('confirmed')["confirmed"].values[-10:],color="darkcyan")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Confirmed Cases",fontsize=18)
plt.title("Top 10 States: India (Confirmed Cases)",fontsize=20)
plt.grid(alpha=0.3)
plt.savefig(out+'Top 10 States_India (Confirmed Cases).png')
In [88]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_india.sort_values('deaths')["deaths"].index[-10:],df_india.sort_values('deaths')["deaths"].values[-10:],color="crimson")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Deaths",fontsize=18)
plt.title("Top 10 States: India (Deaths Cases)",fontsize=20)
plt.grid(alpha=0.3)
plt.savefig(out+'Top 10 States_India (Deaths Cases).png')
Correlation¶
In [89]:
df_india.corr().style.background_gradient(cmap='Reds').format("{:.2f}")
Out[89]:
Map Visualization (Statewise Inida)¶
In [90]:
# Adding Location data (Latitude,Longitude)
locations = {
"Kerala" : [10.8505,76.2711],
"Maharashtra" : [19.7515,75.7139],
"Karnataka": [15.3173,75.7139],
"Telangana": [18.1124,79.0193],
"Uttar Pradesh": [26.8467,80.9462],
"Rajasthan": [27.0238,74.2179],
"Gujarat":[22.2587,71.1924],
"Delhi" : [28.7041,77.1025],
"Punjab":[31.1471,75.3412],
"Tamil Nadu": [11.1271,78.6569],
"Haryana": [29.0588,76.0856],
"Madhya Pradesh":[22.9734,78.6569],
"Jammu and Kashmir":[33.7782,76.5762],
"Ladakh": [34.1526,77.5770],
"Andhra Pradesh":[15.9129,79.7400],
"West Bengal": [22.9868,87.8550],
"Bihar": [25.0961,85.3131],
"Chhattisgarh":[21.2787,81.8661],
"Chandigarh":[30.7333,76.7794],
"Uttarakhand":[30.0668,79.0193],
"Himachal Pradesh":[31.1048,77.1734],
"Goa": [15.2993,74.1240],
"Odisha":[20.9517,85.0985],
"Andaman and Nicobar Islands": [11.7401,92.6586],
"Puducherry":[11.9416,79.8083],
"Manipur":[24.6637,93.9063],
"Mizoram":[23.1645,92.9376],
"Assam":[26.2006,92.9376],
"Meghalaya":[25.4670,91.3662],
"Tripura":[23.9408,91.9882],
"Arunachal Pradesh":[28.2180,94.7278],
"Jharkhand" : [23.6102,85.2799],
"Nagaland": [26.1584,94.5624],
"Sikkim": [27.5330,88.5122],
"Dadra and Nagar Haveli":[20.1809,73.0169],
"Lakshadweep":[10.5667,72.6417],
"Daman and Diu":[20.4283,72.8397]
}
df_india["Lat"] = ""
df_india["Long"] = ""
for index in df_india.index :
if index in locations:
df_india.loc[df_india.index == index,"Lat"] = locations[index][0]
df_india.loc[df_india.index == index,"Long"] = locations[index][1]
else:
df_india.drop([index],inplace=True)
In [91]:
india = folium.Map(location=[23,80], zoom_start=4,max_zoom=6,min_zoom=4,height=500,width="80%")
for i in range(0,len(df_india[df_india['confirmed']>0].index)):
folium.Circle(
location=[df_india.iloc[i]['Lat'], df_india.iloc[i]['Long']],
tooltip = "<h5 style='text-align:center;font-weight: bold'>"+df_india.iloc[i].name+"</h5>"+
"<hr style='margin:10px;'>"+
"<ul style='color: #444;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
"<li>Confirmed: "+str(df_india.iloc[i]['confirmed'])+"</li>"+
"<li>Active: "+str(df_india.iloc[i]['active'])+"</li>"+
"<li>Recovered: "+str(df_india.iloc[i]['recovered'])+"</li>"+
"<li>Deaths: "+str(df_india.iloc[i]['deaths'])+"</li>"+
"<li>Mortality Rate: "+str(np.round(df_india.iloc[i]['Mortality Rate (per 100)'],2))+"</li>"+
"</ul>"
,
radius=(int(np.log2(df_india.iloc[i]['confirmed']+1)))*15000,
color='#ff6600',
fill_color='#ff8533',
fill=True).add_to(india)
india
Out[91]:
COVID-19 : USA¶
In [92]:
date_usa = datetime.strptime(df_confirmed.columns[-1],'%m/%d/%y').strftime("%m-%d-%Y")
df_temp = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/"+date_usa+".csv")
df_usa = df_temp.loc[df_temp["Country_Region"]== "US"]
df_usa = df_usa.rename(columns={"Admin2":"County"})
In [93]:
total = df_usa.sum()
total.name = "Total"
pd.DataFrame(total).transpose().loc[:,["Confirmed","Deaths"]].style.background_gradient(cmap='Purples',axis=1)
Out[93]:
In [94]:
df_usa.loc[:,["Confirmed","Deaths","Province_State"]].groupby(["Province_State"]).sum().sort_values("Confirmed",ascending=False).style.background_gradient(cmap='Blues',subset=["Confirmed"]).background_gradient(cmap='Reds',subset=["Deaths"])
Out[94]:
Top 10 affected Cities or Counties:¶
In [95]:
df_usa.loc[:,["Province_State","Confirmed","Deaths","County"]].sort_values("Confirmed",ascending=False).set_index("County")[:10].style.background_gradient(cmap='Blues',subset=["Confirmed"])\
.background_gradient(cmap='Reds',subset=["Deaths"])
Out[95]:
Most Affected States: USA¶
In [96]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_usa.groupby(["Province_State"]).sum().sort_values('Confirmed')["Confirmed"].index[-10:],df_usa.groupby(["Province_State"]).sum().sort_values('Confirmed')["Confirmed"].values[-10:],color="darkcyan")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Confirmed Cases",fontsize=18)
plt.title("Top 10 States: USA (Confirmed Cases)",fontsize=20)
plt.grid(alpha=0.3)
plt.savefig(out+'Top 10 States_USA (Confirmed Cases).png')
In [97]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_usa.groupby(["Province_State"]).sum().sort_values('Deaths')["Deaths"].index[-10:],df_usa.groupby(["Province_State"]).sum().sort_values('Deaths')["Deaths"].values[-10:],color="crimson")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Deaths",fontsize=18)
plt.title("Top 10 States: USA (Deaths Cases)",fontsize=20)
plt.grid(alpha=0.3)
plt.savefig(out+'Top 10 States_USA (Deaths Cases).png')
Map Visualization (Demographic)¶
In [98]:
# url = "https://raw.githubusercontent.com/Subhash9325/GeoJson-Data-of-Indian-States/master/Indian_States"
# state_json = requests.get(url).json()
df_usa = df_usa.replace(np.nan, 0, regex=True)
usa = folium.Map(location=[37, -102], zoom_start=4,max_zoom=9,min_zoom=4)
for i in np.int32(np.asarray(df_usa[df_usa['Confirmed'] > 0].index)):
folium.Circle(
location=[df_usa.loc[i]['Lat'], df_usa.loc[i]['Long_']],
tooltip = "<h5 style='text-align:center;font-weight: bold'>"+df_usa.loc[i]['Province_State']+"</h5>"+
"<div style='text-align:center;'>"+str(np.nan_to_num(df_usa.loc[i]['County']))+"</div>"+
"<hr style='margin:10px;'>"+
"<ul style='color: #444;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
"<li>Confirmed: "+str(df_usa.loc[i]['Confirmed'])+"</li>"+
"<li>Active: "+str(df_usa.loc[i]['Active'])+"</li>"+
"<li>Recovered: "+str(df_usa.loc[i]['Recovered'])+"</li>"+
"<li>Deaths: "+str(df_usa.loc[i]['Deaths'])+"</li>"+
"<li>Mortality Rate: "+str(np.round(df_usa.loc[i]['Deaths']/(df_usa.loc[i]['Confirmed']+1)*100,2))+"</li>"+
"</ul>"
,
radius=int((np.log2(df_usa.loc[i]['Confirmed']+1))*6000),
color='#ff6600',
fill_color='#ff8533',
fill=True).add_to(usa)
usa
Out[98]:
In [99]:
state_geo = requests.get('https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json').json()
county_geo = requests.get('https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us_counties_20m_topo.json').json()
# county_geo
In [100]:
data_temp = df_usa.groupby(["Province_State"]).sum().reset_index().drop(["Lat","Long_"],axis=1)
data_temp["Confirmed_log"] = np.log10(data_temp["Confirmed"]+1)
bins = list(data_temp['Confirmed_log'].quantile([0, 0.25, 0.5, 0.75,0.95 ,1]))
m = folium.Map(location=[37, -102], zoom_start=4,max_zoom=6,min_zoom=3)
# Add the color for the chloropleth:
folium.Choropleth(
geo_data=state_geo,
name='choropleth',
data = data_temp,
columns=['Province_State', 'Confirmed_log'],
key_on='feature.properties.name',
fill_color='Reds',
fill_opacity=0.7,
line_opacity=0.2,
bins = bins,
reset=True,
legend_name='Confirmed cases log (10^x)'
).add_to(m)
folium.LayerControl().add_to(m)
legend_html = "<div style='padding:10px;background-color:rgba(255,255,255,0.5);position:fixed;bottom:20px;left:20px;z-index:1000'>"
legend_html += "<div style='width:100%;text-align:center;'><h4>Index for Legend</h4></div><hr style='border-top-color: rgba(25,25,25,0.5);'>"
legend_html += "<ul style='margin:0;padding:0;color: #555;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"
for i in bins:
legend_html += "<li style='margin:0;padding:0;line-height: 0;'>Value "+str(np.round(i,2))+" : "+str(int(10**i)-1)+"</li><br>"
legend_html += "</ul></div>"
m.get_root().html.add_child(folium.Element(legend_html))
m
Out[100]:
Affected Counties : USA¶
In [101]:
# binsurl = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'
# county_data = f'{url}/us_county_data.csv'
# county_geo = f'{url}/us_counties_20m_topo.json'
data_temp = df_usa.groupby(["FIPS"]).sum().reset_index().drop(["Lat","Long_"],axis=1)
data_temp["Confirmed_log"] = np.log10(data_temp["Confirmed"]+1)
df_usa_series = data_temp.set_index('FIPS')['Confirmed_log']
colorscale = branca.colormap.linear.Reds_09.scale(0,data_temp["Confirmed_log"].max()-1)
# print(df_usa_series.max())
def style_function(feature):
employed = df_usa_series.get(int(feature['id'][-5:]), 0)
return {
'fillOpacity': 0.5,
'weight': 0,
'fillColor': '#black' if employed is None else colorscale(employed)
}
m = folium.Map(
location=[37, -102],
tiles='cartodbpositron',
zoom_start=4,
min_zoom=3,
max_zoom=7
)
folium.TopoJson(
county_geo,
'objects.us_counties_20m',
style_function=style_function
).add_to(m)
m
Out[101]:
Prediction Curve for USA¶
In [102]:
# Visible = Input(shape=(1,))
# Dense_l1 = Dense(80,name="Dense_l1")(Visible)
# LRelu_l1 = LeakyReLU(name = "LRelu_l1")(Dense_l1)
# Dense_l2 = Dense(80,name = "Dense_l2")(LRelu_l1)
# LRelu_l2 = LeakyReLU(name = "LRelu_l2")(Dense_l2)
# Dense_l3 = Dense(1,name="Dense_l3")(LRelu_l2)
# LRelu_l3 = LeakyReLU(name = "Output")(Dense_l3)
# model_usa_c = models.Model(inputs=Visible, outputs=LRelu_l3)
# model_usa_c.compile(optimizer=Adam(lr=0.0001),
# loss='mean_squared_error',
# metrics=['accuracy'])
# model_usa_c.summary()
In [103]:
# model_usa_c.save('model_usa_c.h5')
In [104]:
df_temp = df_confirmed.groupby(["country"]).sum()
df_temp = np.asarray(df_temp[df_temp.index == "USA"].iloc[:,2:])[0]
data_y = np.log10(df_temp).astype("float32")
data_x = np.arange(1,len(data_y)+1)
# data_x.shape[0]
In [105]:
# epochs = 5000
# model_usa_c.fit(data_x.reshape([data_x.shape[0],1]),data_y.reshape([data_x.shape[0],1]),epochs=epochs,batch_size=64)
In [106]:
model_usa_c = models.load_model("model_usa_c_v2.h5")
model_usa_c.summary()
In [107]:
lakh = 100000
prediction_days = 7
df_temp = df_confirmed.groupby(["country"]).sum()
temp_data = np.asarray(df_temp[df_temp.index == "USA"].iloc[:,2:])[0]
data = np.power(10,model_usa_c.predict(np.arange(1,len(temp_data)+prediction_days+1)))
# temp_data = df_confirmed.iloc[:,5:].sum(axis =0)
f = plt.figure(figsize=(12,8))
ax = f.add_subplot(111)
date = np.arange(0,len(temp_data))
marker_style = dict(linewidth=3, linestyle='-', marker='o',markersize=7, markerfacecolor='#ffffff')
plt.plot(date,temp_data,"-.",color="darkcyan",**marker_style, label="Actual Curve")
date = np.arange(0,len(data))
plt.plot(date,data,"-.",color="orangered",label="Predicted Curve")
nextdays = [(datetime.strptime(d[-1],'%d %b')+timedelta(days=i)).strftime("%d %b") for i in range(1,prediction_days+1)]
total =d+nextdays
text = "Prediction for next "+str(prediction_days) +" days:\n"
for i in range(prediction_days):
text += nextdays[i]+" : "+str(np.round(data[-1*(prediction_days-i)],-3)[0]/lakh)+" L\n"
plt.text(0.02, 0.78, text, fontsize=17, horizontalalignment='left', verticalalignment='top', transform=ax.transAxes,bbox=dict(facecolor='white', alpha=0.4))
# X-axis
plt.xticks(list(np.arange(0,len(total),int(len(total)/5))),total[:-1:int(len(total)/5)]+[total[-1]])
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=14)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.7", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.9", which = 'minor')
# Plot Title
plt.title("COVID-19 Next 7 day Prediction Curve-USA Confirmed Cases",{'fontsize':22})
# Axis Lable
plt.xlabel("Date",fontsize =18)
plt.ylabel("Number of Confirmed Cases",fontsize =18)
plt.yscale("log")
plt.legend(fontsize =18)
plt.tick_params(labelsize = 13)
plt.savefig(out+"Prediction Curve-Confirmed (USA).png")
plt.show()
This analysis is made using Machine Learning to predict the best fit curve. The actual curve is following the expected trajectory, if this happens for the next five days, then it may be possible that confirmed cases reach about 400K+. It is a very fast growth, and the actual figure may vary significantly. I will update the prediction curve as more data available.
Valuable Feedback¶
NOTE : Some Features of the notebook has been depricated such as analysis of recovered and active cases. Check out version 17¶
- Your FEEDBACK is much APPRECIATED
- Please UPVOTE if you LIKE this NOTEBOOK
- COMMENT BELOW if you have any DOUBTS or you found any ERROR in NOTEBOOK